Intrinsic connectivity reveals functionally distinct cortico-hippocampal networks in the human brain

  1. Alexander J. Barnett
  2. Walter Reilly
  3. Halle R. Dimsdale-Zucker
  4. Eda Mizrak
  5. Zachariah Reagh
  6. Charan Ranganath

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Abstract

Episodic memory depends on interactions between the hippocampus and interconnected neocortical regions. Here, using data-driven analyses of resting-state functional magnetic resonance imaging (fMRI) data, we identified the networks that interact with the hippocampus—the default mode network (DMN) and a “medial temporal network” (MTN) that included regions in the medial temporal lobe (MTL) and precuneus. We observed that the MTN plays a critical role in connecting the visual network to the DMN and hippocampus. The DMN could be further divided into 3 subnetworks: a “posterior medial” (PM) subnetwork comprised of posterior cingulate and lateral parietal cortices; an “anterior temporal” (AT) subnetwork comprised of regions in the temporopolar and dorsomedial prefrontal cortex; and a “medial prefrontal” (MP) subnetwork comprised of regions primarily in the medial prefrontal cortex (mPFC). These networks vary in their functional connectivity (FC) along the hippocampal long axis and represent different kinds of information during memory-guided decision-making. Finally, a Neurosynth meta-analysis of fMRI studies suggests new hypotheses regarding the functions of the MTN and DMN subnetworks, providing a framework to guide future research on the neural architecture of episodic memory.

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  1. Version published to 10.1371/journal.pbio.3001275
  2. eLife

    ###Reviewer #3:

    In this paper, Barnett and colleagues used network-based, data-driven analyses to characterize how the default mode network (DMN) and the Medial Temporal Network (MTN) interact with the hippocampus. First, the authors confirmed previous findings that the MTN is a distinct network from the DMN. Second, the authors identified three subnetworks of the DMN that differ from each other based on their connectivity profiles. They further investigated cross-network and intra-network dynamics during rest and also the representational similarity of patterns within these networks during a memory retrieval task. Finally, they used meta-analytic analyses to develop hypotheses about the specific cognitive functions of the MTN and DMN subnetworks.

    Major comments:

    1. One noteworthy aspect of this paper is that the networks identified by the current investigation do not map on perfectly to a previous framework outlined by the senior author (the AT-PM framework; Ranganath and Ritchey, 2012). I think that readers of this work will be very curious to hear about this update, and I think that the similarities and differences between the AT-PM framework and the current findings should be made crystal clear. For example, perhaps a schematic could be used to visually depict the similarities and differences.

    2. In addition to this suggested visualization, I think that memory scholars that are familiar with the AT-PM framework will be curious to know how these results can update the current thinking of how different brain networks organize memories and perform different types of cognitive functions. The meta-analysis partially does this, but one is left wondering about how this changes our updates the field's understanding of how specific types of memory (e.g. object versus scene memory as in Maass et al., Brain, 2019) are supported.

    3. The authors state in the methods, "This sample size is comparable to the cohort sample sizes from the seminal Power et al., study investigating functional brain organization." I think a bit more can be said about the effect sizes reported in the previous literature (which might be inflated due to publication bias), and the power to detect such effect sizes (or smaller) here.

    4. I found the results reported in the section "Regions within the same community represent similar kinds of information during a memory task" difficult to follow. Moreover, I was not sure what this analysis provides beyond the resting state analyses. This paper would be strengthened if these analyses were linked to behavioral performance on the memory retrieval task.

    5. I was surprised to see that the Anterior Hippocampus was more highly correlated (numerically) to the DMN (Supplementary Table 1) and the MP and PM sub-networks (Figure 4) compared to the MTN network. Is this difference statistically significant, and, if so, do the authors think that this difference is meaningful?

    6. Tau spreading models have been demonstrated to follow patterns of function connectivity (Franzmeier et al., Nature Comms, 2020). The authors may wish to comment on the relevance of these findings to different patterns of tau accumulation in different types of dementia.

  3. eLife

    ###Reviewer #2:

    Overall, I thought that the topic addressed and approaches used were interesting and in particular I appreciated the motivation of relating data-driven analyses of resting state data to existing theoretical frameworks and task-based data. As described below, I believe the manuscript could be strengthened with additional comparison to past work as well as addressing a potential methodological issue.

    1. As noted by the authors, past work has used data-driven approaches on resting state data to subdivide the default mode network. The manuscript would be strengthened by highlighting the similarities/differences of the current work with such past work. In terms of revealing subnetworks, Is it believed that some aspects of the data acquisition/delineation methods employed here are preferable? MTL signal dropout was mentioned in the discussion, but was this a major motivating factor? Might there be any way of quantifying or tabulating the differences between the proposed subdivisions here and other efforts in order to help bridge the current findings to past work and to assess how and why the current results might differ?

    2. The motivation to link data-driven network clustering approaches (e.g. the MTN and DMN subnetworks found here) with more hypothesis-driven approaches (e.g. the PM/AT framework) is a key strength of the study, although the findings and conclusions drawn about the relationship were a little difficult to fully understand. For example, how functionally distinct are the MTN and the PM/AT DMN subnetworks given that the PM/AT framework highlights the distinct contributions of subregions of the MTN (e.g. PHC/PRC)? Is it thought that there is a distinction between PM/AT pathways that spans DMN and MTN but is not captured here or do the findings suggest that a better distinction in terms of understanding hippocampal-based memory in the brain is between DMN subregions and MTN? Relatedly, might it be possible that the DMN subnetworks connectivity with the hippocampus is mediated by MTL subregions? More generally, this comment is intended to probe the authors as to whether they believe that the data-driven and hypothesis-driven are reconcilable or if they are arguing that the data-driven approach is preferable.

    3. To what degree might the spatial proximity of the ROIs influence the results of the various analyses? In particular, I wonder if the analyses done using pattern similarity might be influenced by partial non-independence of adjacent ROIs. That is, adjacent ROIs might have correlated pattern similarity due to smoothing and other sources of voxelwise spatial non-independence, and so insofar as there are more nearby ROIs within networks than across networks, it might influence the observed results. Similar concerns might be applicable to the Participation analysis, but seem less obvious.

  4. eLife

    ###Reviewer #1:

    This paper characterizes resting state functional connectivity across the brain and within memory networks, evaluates whether similar networks arise in a memory-guided decision-making task, and collects descriptions of the function of these networks in prior imaging studies. The authors find that the DMN and a Medial Temporal Network (MTN) can be differentiated, and that there are three subnetworks within the DMN that interact differently with different parts of the hippocampus and that have been ascribed different kinds of functions in prior imaging studies.

    The paper provides a systematic overview and re-examination of multiple approaches that have been used before to characterize networks across the brain and those focused on memory systems. My overall sense is the paper will be very useful to the cognitive neuroscience / memory communities but does not present a substantial theoretical advance. I am also concerned about the interpretation of the memory task connectivity data, as described below.

    Major comments:

    -It seems possible to me that the trial-by-trial RSA analyses run on the task data are picking up on basically the same signal as the functional connectivity resting state analyses. If the authors ran the RSA analyses TR by TR on the resting state data, would that pick up the same structure? Similarly, would the functional connectivity analyses on the task data explain the same variance as the RSA? Univariate signals can drive RSA effects, so careful analyses would need to be done to demonstrate that these methods are picking up on different aspects of the interactions between these regions. Relatedly, if the authors have access to a non-memory task dataset, perhaps it could be useful to show that the results are different in that case.

    -The results are displayed on surfaces, but I think (but am not sure) that all the analyses were done in the volume. Given the interest in the hippocampus and its connectivity, it would be very useful to see results displayed in the volume in addition to (or replacing) the surfaces.

    -By eye, the MP network as shown in Fig 2 looks much less coherent than the other two. It is difficult to see much cluster structure there at all. I am therefore unsure how confident to feel in the existence of this as a distinct network.

  5. eLife

    ##Preprint Review

    This preprint was reviewed using eLife’s Preprint Review service, which provides public peer reviews of manuscripts posted on bioRxiv for the benefit of the authors, readers, potential readers, and others interested in our assessment of the work. This review applies only to version 1 of the manuscript.

    ###Summary:

    All reviewers felt that this work represents a useful contribution to the literature, relating different perspectives on the nature of interactions between brain areas and how these interactions may support memory, but that it does not offer a substantial theoretical advance beyond prior work. The reviewers also raise some methodological concerns that the authors may wish to consider.

  6. Version published to 10.1101/2020.06.09.142166 on bioRxiv